@inproceedings{Cusumano-Towner:2019,
 author = {Cusumano-Towner, Marco F. and Saad, Feras A. and Lew, Alexander K. and Mansinghka, Vikash K.},
 title = {Gen: A General-purpose Probabilistic Programming System with Programmable Inference},
 booktitle = {Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation},
 series = {PLDI 2019},
 year = {2019},
 isbn = {978-1-4503-6712-7},
 location = {Phoenix, AZ, USA},
 pages = {221--236},
 numpages = {16},
 url = {http://doi.acm.org/10.1145/3314221.3314642},
 doi = {10.1145/3314221.3314642},
 acmid = {3314642},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Markov chain Monte Carlo, Probabilistic programming, sequential Monte Carlo, variational inference},
} 

@software{Blaom:2019,
  author       = {Anthony Blaom and
                  Franz Kiraly and
                  Thibaut Lienart and
                  Sebastian Vollmer},
  title        = {alan-turing-institute/MLJ.jl: v0.5.3},
  month        = nov,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v0.5.3},
  doi          = {10.5281/zenodo.3541506},
  url          = {https://doi.org/10.5281/zenodo.3541506}
}

@article{Meurer:2017,
 title = {SymPy: symbolic computing in Python},
 author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
 year = 2017,
 month = jan,
 keywords = {Python, Computer algebra system, Symbolics},
 abstract = {
            SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
         },
 volume = 3,
 pages = {e103},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.103},
 doi = {10.7717/peerj-cs.103}
}

@inproceedings{ge2018t,
  author    = {Hong Ge and
               Kai Xu and
               Zoubin Ghahramani},
  title     = {Turing: a language for flexible probabilistic inference},
  booktitle = {International Conference on Artificial Intelligence and Statistics,
               {AISTATS} 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands,
               Spain},
  pages     = {1682--1690},
  year      = {2018},
  url       = {http://proceedings.mlr.press/v84/ge18b.html},
  biburl    = {https://dblp.org/rec/bib/conf/aistats/GeXG18},
}

@article{arviz_2019,
        title = {{ArviZ} a unified library for exploratory analysis of {Bayesian} models in {Python}},
        author = {Kumar, Ravin and Carroll, Colin and Hartikainen, Ari and Martin, Osvaldo A.},
        journal = {The Journal of Open Source Software},
        year = {2019},
        doi = {10.21105/joss.01143},
        url = {http://joss.theoj.org/papers/10.21105/joss.01143},
}

@misc{Distributions.jl-2019,
  author       = {Dahua Lin and
                  John Myles White and
                  Simon Byrne and
                  Douglas Bates and
                  Andreas Noack and
                  John Pearson and
                  Alex Arslan and
                  Kevin Squire and
                  David Anthoff and
                  Theodore Papamarkou and
                  Mathieu Besançon and
                  Jan Drugowitsch and
                  Moritz Schauer and
                  other contributors},
  title        = {{JuliaStats/Distributions.jl: a Julia package for probability distributions and associated functions}},
  month        = july,
  year         = 2019,
  doi          = {10.5281/zenodo.2647458},
  url          = {https://doi.org/10.5281/zenodo.2647458}
}

@article{Julia-2017,
    title={Julia: A fresh approach to numerical computing},
    author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B},
    journal={SIAM {R}eview},
    volume={59},
    number={1},
    pages={65--98},
    year={2017},
    publisher={SIAM},
    doi={10.1137/141000671}
}

@article{10.7717/peerj-cs.103,
 title = {SymPy: symbolic computing in Python},
 author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
 year = 2017,
 month = jan,
 keywords = {Python, Computer algebra system, Symbolics},
 abstract = {
            SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
         },
 volume = 3,
 pages = {e103},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.103},
 doi = {10.7717/peerj-cs.103}
}
